Guangsen Wang

Artificial Intelligence · 2013–2019

A researcher in the Science corpus: 5 papers; trusts 65 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 85 citations into it; #15,256 of 35,345 by trust here, #15,542 by citations
• Natural Language Processing — 48 citations into it
• Signal Processing — 48 citations into it; #4,032 of 7,572 by trust here, #3,566 by citations
• Speech & Audio — 39 citations into it; #1,022 of 1,076 by trust here, #1,009 by citations
• Model Architectures — 16 citations into it
• Optimization & Training — 12 citations into it; #2,542 of 2,601 by trust here, #2,428 by citations
• Representation & Self-Supervised Learning — 5 citations into it

Whom Guangsen Wang trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Li Deng
3. Dong Yu
4. George E. Dahl
5. Abdel-rahman Mohamed
6. Yoshua Bengio
7. Alex Graves
8. Daniel Povey
9. Diederik P. Kingma
10. Alex Krizhevsky

Who trusts Guangsen Wang overall

1. Li Deng
2. Khe Chai Sim
3. Peidong Wang
4. Dong Yu
5. Leonardo Badino
6. Jinyu Li
7. Chao Weng
8. Jia Cui

Papers from Guangsen Wang's perspective

Ordered by how strongly the researchers Guangsen Wang trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Improving Attention Based Sequence-to-Sequence Models for End-to-End English Conversational Speech Recognition (2018) — 59 citations
• Regression-Based Context-Dependent Modeling of Deep Neural Networks for Speech Recognition (2014) — 19 citations
• Context-dependent modelling of deep neural network using logistic regression (2013) — 4 citations
• Improving Attention-Based End-to-End ASR Systems with Sequence-Based Loss Functions (2018) — 10 citations
• Speech-XLNet: Unsupervised Acoustic Model Pretraining For Self-Attention Networks (2019) — 54 citations